Who is Who in Phylogenetic Networks

Publications related to 'Program MC-Net' : MC-Net is a Matlab program to compute, from a distance matrix, a circular split system which can be visualized with SplitsTree. Available at http://bioinf.cs.ipm.ac.ir/softwares/mc.net/.   Order by:   Type | Year

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abstract-network circular-split-system from-distances heuristic phylogenetic-network Program-MC-Net Program-SplitsTree software split split-network

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1 Changiz Eslahchi, Mahnaz Habibi, Reza Hassanzadeh and Ehsan Mottaghi. MC-Net: a method for the construction of phylogenetic networks based on the Monte-Carlo method. In BMCEB, Vol. 10:254, 2010. Keywords: abstract network, circular split system, from distances, heuristic, phylogenetic network, Program MC-Net, Program SplitsTree, software, split, split network. Note: http://dx.doi.org/10.1186/1471-2148-10-254.         Toggle abstract "Background. A phylogenetic network is a generalization of phylogenetic trees that allows the representation of conflicting signals or alternative evolutionary histories in a single diagram. There are several methods for constructing these networks. Some of these methods are based on distances among taxa. In practice, the methods which are based on distance perform faster in comparison with other methods. The Neighbor-Net (N-Net) is a distance-based method. The N-Net produces a circular ordering from a distance matrix, then constructs a collection of weighted splits using circular ordering. The SplitsTree which is a program using these weighted splits makes a phylogenetic network. In general, finding an optimal circular ordering is an NP-hard problem. The N-Net is a heuristic algorithm to find the optimal circular ordering which is based on neighbor-joining algorithm. Results. In this paper, we present a heuristic algorithm to find an optimal circular ordering based on the Monte-Carlo method, called MC-Net algorithm. In order to show that MC-Net performs better than N-Net, we apply both algorithms on different data sets. Then we draw phylogenetic networks corresponding to outputs of these algorithms using SplitsTree and compare the results. Conclusions. We find that the circular ordering produced by the MC-Net is closer to optimal circular ordering than the N-Net. Furthermore, the networks corresponding to outputs of MC-Net made by SplitsTree are simpler than N-Net. © 2010 Eslahchi et al; licensee BioMed Central Ltd."